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Purpose: Breast tissue composition is considered to be associated with breast cancer risk. This study aimed to develop a computer-aided classification (CAC) system to automatically classify echotexture patterns as heterogeneous or homogeneous using automated breast ultrasound (ABUS) images. Methods: A CAC system was proposed that can recognize breast echotexture patterns in ABUS images. For each case, the echotexture pattern was assessed by two expert radiologists and classified as heterogeneous or homogeneous. After neutrosophic image transformation and fuzzy c-mean clusterings, the lower and upper boundaries of the fibroglandular tissues were defined. Then, the number of hypoechoic regions and histogram features were extracted from the fibroglandular tissues, and the support vector machine model with the leave-one-out cross-validation method was utilized as the classifier. The authors’ database included a total of 208 ABUS images of the breasts of 104 females. Results: The accuracies of the proposed system for the classification of heterogeneous and homogeneous echotexture patterns were 93.48% (43/46) and 92.59% (150/162), respectively, with an overall Az (area under the receiver operating characteristic curve) of 0.9786. The agreement between the radiologists and the proposed system was almost perfect, with a kappa value of 0.814. Conclusions: The use of ABUS and the proposed method can provide quantitative information on the echotexture patterns of the breast and can be used to evaluate whether breast echotexture patterns are associated with breast cancer risk in the future.

Purpose: Triple-negative breast cancer (TNBC), an aggressive subtype, is frequently misclassified as fibroadenoma due to benign morphologic features on breast ultrasound (US). This study aims to develop a computer-aided diagnosis (CAD) system based on texture features for distinguishing between TNBC and benign fibroadenomas in US images. Methods: US images of 169 pathology-proven tumors (mean size, 1.65 cm; range, 0.7–3.0 cm) composed of 84 benign fibroadenomas and 85 TNBC tumors are used in this study. After a tumor is segmented out using the level-set method, morphological, conventional texture, and multiresolution gray-scale invariant texture feature sets are computed using a best-fitting ellipse, gray-level co-occurrence matrices, and the ranklet transform, respectively. The linear support vector machine with leave-one-out cross-validation schema is used as a classifier, and the diagnostic performance is assessed with receiver operating characteristic curve analysis. Results: The Az values of the morphology, conventional texture, and multiresolution gray-scale invariant texture feature sets are 0.8470 [95% confidence intervals (CIs), 0.7826–0.8973], 0.8542 (95% CI, 0.7911–0.9030), and 0.9695 (95% CI, 0.9376–0.9865), respectively. The Az of the CAD system based on the combined feature sets is 0.9702 (95% CI, 0.9334–0.9882). Conclusions: The CAD system based on texture features extracted via the ranklet transform may be useful for improving the ability to discriminate between TNBC and benign fibroadenomas.

Screening ultrasound (US) can increase the detection of breast cancer. However, little is known about the clinicopathologic characteristics of breast cancers detected by screening US. A search of the database for patients with breast cancer yielded a dataset in 6837 women who underwent breast surgery at Seoul National University Hospital (Korea). Of 6837 women, 1047 were asymptomatic and had a non-palpable cancer. Two hundred fifty-four women with 256 cancers detected by US (US-detected cancer) and 793 women with 807 cancers detected by mammography (MG-detected cancer) were identified. The imaging, clinicopathologic, and molecular data were reviewed. Univariate and multivariate analyses were carried out. Women with US-detected cancer were younger and were more likely to undergo breast-conserving surgery and to have node-negative invasive cancer (P0.0001). By multivariate analysis, the significant independent characteristics were tumor size, mammographic density, final assessment category according to the American College of Radiology Breast Imaging Reporting and Data System, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and molecular subtype. Compared to tumors that were2 cm in size, tumors that were ≤ 1 cm in size were 2.2-fold more likely to be US-detected cancers (P = 0.02). Compared to the luminal A subtype tumors (estrogen receptor [ER]+, PR+, HER2-), luminal B subtype tumors (ER+, PR+, HER2+) were less likely to be in the US-detected cancer group (P0.01). Women with dense breasts were more likely to have US-detected cancer (P0.01) versus those with non-dense breasts. Screening US-detected cancers were less likely to be diagnosed as category 5 instead of category 4 (P0.01). In conclusion, women with US-detected breast cancer are more likely to have small-sized invasive cancer and more likely associated with the luminal A subtype.